--- a
+++ b/Feature Extraction/extractCSP.m
@@ -0,0 +1,41 @@
+function features = extractCSP(EEGSignals, CSPMatrix, nbFilterPairs)
+%
+%extract features from an EEG data set using the Common Spatial Patterns (CSP) algorithm
+%
+%Input:
+%EEGSignals: the EEGSignals from which extracting the CSP features. These signals
+%are a structure such that:
+%   EEGSignals.x: the EEG signals as a [Ns * Nc * Nt] Matrix where
+%       Ns: number of EEG samples per trial
+%       Nc: number of channels (EEG electrodes)
+%       nT: number of trials
+%   EEGSignals.y: a [1 * Nt] vector containing the class labels for each trial
+%   EEGSignals.s: the sampling frequency (in Hz)
+%CSPMatrix: the CSP projection matrix, learnt previously (see function learnCSP)
+%nbFilterPairs: number of pairs of CSP filters to be used. The number of
+%   features extracted will be twice the value of this parameter. The
+%   filters selected are the one corresponding to the lowest and highest
+%   eigenvalues
+%
+%Output:
+%features: the features extracted from this EEG data set 
+%   as a [Nt * (nbFilterPairs*2 + 1)] matrix, with the class labels as the
+%   last column   
+%
+%initializations
+
+nbTrials = size(EEGSignals.x,3);
+features = zeros(nbTrials, 2*nbFilterPairs+1);
+Filter = CSPMatrix([1:nbFilterPairs (end-nbFilterPairs+1):end],:);
+
+%extracting the CSP features from each trial
+for t=1:nbTrials    
+    %projecting the data onto the CSP filters    
+    projectedTrial = Filter * EEGSignals.x(:,:,t)';    
+    
+    %generating the features as the log variance of the projected signals
+    variances = var(projectedTrial,0,2);    
+    for f=1:length(variances)
+        features(t,f) = log(1+variances(f));
+    end
+end
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